1. Load the Dataset

import pandas as pd

# Load the dataset
data = pd.read_csv(r"C:\Users\yoshu\Downloads\Boxify Dataset - Data Analyst Bootcamp.csv")
data.head()

	Order	File_Type	SKU_number	SoldFlag	SoldCount	MarketingType	ReleaseNumber	New_Release_Flag	StrengthFactor	PriceReg	ReleaseYear	ItemCount	LowUserPrice	LowNetPrice
0	2	Historical	1737127	0.0	0.0	D	15	1	682743.0	44.99	2015	8	28.97	31.84
1	3	Historical	3255963	0.0	0.0	D	7	1	1016014.0	24.81	2005	39	0.00	15.54
2	4	Historical	612701	0.0	0.0	D	0	0	340464.0	46.00	2013	34	30.19	27.97
3	6	Historical	115883	1.0	1.0	D	4	1	334011.0	100.00	2006	20	133.93	83.15
4	7	Historical	863939	1.0	1.0	D	2	1	1287938.0	121.95	2010	28	4.00	23.99

2. Summary of the dataset

# Summary of the dataset
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 198917 entries, 0 to 198916
Data columns (total 14 columns):
 #   Column            Non-Null Count   Dtype  
---  ------            --------------   -----  
 0   Order             198917 non-null  int64  
 1   File_Type         198917 non-null  object 
 2   SKU_number        198917 non-null  int64  
 3   SoldFlag          75996 non-null   float64
 4   SoldCount         75996 non-null   float64
 5   MarketingType     198917 non-null  object 
 6   ReleaseNumber     198917 non-null  int64  
 7   New_Release_Flag  198917 non-null  int64  
 8   StrengthFactor    198917 non-null  float64
 9   PriceReg          198917 non-null  float64
 10  ReleaseYear       198917 non-null  int64  
 11  ItemCount         198917 non-null  int64  
 12  LowUserPrice      198917 non-null  float64
 13  LowNetPrice       198917 non-null  float64
dtypes: float64(6), int64(6), object(2)
memory usage: 21.2+ MB

 data.describe()

	Order	SKU_number	SoldFlag	SoldCount	ReleaseNumber	New_Release_Flag	StrengthFactor	PriceReg	ReleaseYear	ItemCount	LowUserPrice	LowNetPrice
count	198917.000000	1.989170e+05	75996.000000	75996.000000	198917.000000	198917.000000	1.989170e+05	198917.000000	198917.000000	198917.000000	198917.000000	198917.000000
mean	106483.543242	8.613626e+05	0.171009	0.322306	3.412202	0.642248	1.117115e+06	90.895243	2006.016414	41.426283	30.982487	46.832053
std	60136.716784	8.699794e+05	0.376519	1.168615	3.864243	0.479340	1.522090e+06	86.736367	9.158331	37.541215	69.066155	128.513236
min	2.000000	5.000100e+04	0.000000	0.000000	0.000000	0.000000	6.275000e+00	0.000000	0.000000	0.000000	0.000000	0.000000
25%	55665.000000	2.172520e+05	0.000000	0.000000	1.000000	0.000000	1.614188e+05	42.000000	2003.000000	21.000000	4.910000	17.950000
50%	108569.000000	6.122080e+05	0.000000	0.000000	2.000000	1.000000	5.822240e+05	69.950000	2007.000000	32.000000	16.080000	33.980000
75%	158298.000000	9.047510e+05	0.000000	0.000000	5.000000	1.000000	1.430083e+06	116.000000	2011.000000	50.000000	40.240000	55.490000
max	208027.000000	3.960788e+06	1.000000	73.000000	99.000000	1.000000	1.738445e+07	12671.480000	2018.000000	2542.000000	14140.210000	19138.790000

data.columns

Index(['Order', 'File_Type', 'SKU_number', 'SoldFlag', 'SoldCount',
       'MarketingType', 'ReleaseNumber', 'New_Release_Flag', 'StrengthFactor',
       'PriceReg', 'ReleaseYear', 'ItemCount', 'LowUserPrice', 'LowNetPrice'],
      dtype='object')

The dataset contains the following columns: 1. Order: Integer, unique identifier for the order. 2. File_Type: Categorical, type of file (e.g., "Historical"). 3. SKU_number: Integer, unique identifier for the SKU (Stock Keeping Unit). 4. SoldFlag: Float, indicates whether the product was sold (1) or not (0). 5. SoldCount: Float, number of items sold. 6. MarketingType: Categorical, type of marketing used. 7. ReleaseNumber: Integer, release sequence number. 8. New_Release_Flag: Integer, indicates if the item is a new release (1) or not (0). 9. StrengthFactor: Float, strength factor of the product. 10. PriceReg: Float, regular price of the product. 11. ReleaseYear: Integer, year of release. 12. ItemCount: Integer, number of items available. 13. LowUserPrice: Float, lowest user price. 14. LowNetPrice: Float, lowest net price.

3. Data Cleaning and Preparation

# Handling missing values
# For SoldFlag and SoldCount, let's replace missing values with 0 (indicating no sale)
data['SoldFlag'].fillna(0, inplace=True)
data['SoldCount'].fillna(0, inplace=True)

# Verifying the changes
missing_values_summary = data.isnull().sum()

# Checking for any inconsistencies or outliers in numerical columns
numerical_columns = ['Order', 'SKU_number', 'SoldFlag', 'SoldCount', 'ReleaseNumber', 
                     'New_Release_Flag', 'StrengthFactor', 'PriceReg', 'ReleaseYear', 
                     'ItemCount', 'LowUserPrice', 'LowNetPrice']
numerical_summary = data[numerical_columns].describe()

missing_values_summary, numerical_summary

(Order               0
 File_Type           0
 SKU_number          0
 SoldFlag            0
 SoldCount           0
 MarketingType       0
 ReleaseNumber       0
 New_Release_Flag    0
 StrengthFactor      0
 PriceReg            0
 ReleaseYear         0
 ItemCount           0
 LowUserPrice        0
 LowNetPrice         0
 dtype: int64,
                Order    SKU_number       SoldFlag      SoldCount  \
 count  198917.000000  1.989170e+05  198917.000000  198917.000000   
 mean   106483.543242  8.613626e+05       0.065334       0.123137   
 std     60136.716784  8.699794e+05       0.247115       0.739101   
 min         2.000000  5.000100e+04       0.000000       0.000000   
 25%     55665.000000  2.172520e+05       0.000000       0.000000   
 50%    108569.000000  6.122080e+05       0.000000       0.000000   
 75%    158298.000000  9.047510e+05       0.000000       0.000000   
 max    208027.000000  3.960788e+06       1.000000      73.000000   
 
        ReleaseNumber  New_Release_Flag  StrengthFactor       PriceReg  \
 count  198917.000000     198917.000000    1.989170e+05  198917.000000   
 mean        3.412202          0.642248    1.117115e+06      90.895243   
 std         3.864243          0.479340    1.522090e+06      86.736367   
 min         0.000000          0.000000    6.275000e+00       0.000000   
 25%         1.000000          0.000000    1.614188e+05      42.000000   
 50%         2.000000          1.000000    5.822240e+05      69.950000   
 75%         5.000000          1.000000    1.430083e+06     116.000000   
 max        99.000000          1.000000    1.738445e+07   12671.480000   
 
          ReleaseYear      ItemCount   LowUserPrice    LowNetPrice  
 count  198917.000000  198917.000000  198917.000000  198917.000000  
 mean     2006.016414      41.426283      30.982487      46.832053  
 std         9.158331      37.541215      69.066155     128.513236  
 min         0.000000       0.000000       0.000000       0.000000  
 25%      2003.000000      21.000000       4.910000      17.950000  
 50%      2007.000000      32.000000      16.080000      33.980000  
 75%      2011.000000      50.000000      40.240000      55.490000  
 max      2018.000000    2542.000000   14140.210000   19138.790000  )

data.dtypes

Order                 int64
File_Type            object
SKU_number            int64
SoldFlag            float64
SoldCount           float64
MarketingType        object
ReleaseNumber         int64
New_Release_Flag      int64
StrengthFactor      float64
PriceReg            float64
ReleaseYear           int64
ItemCount             int64
LowUserPrice        float64
LowNetPrice         float64
dtype: object

# Convert ReleaseYear to datetime format for better analysis
data['ReleaseYear'] = pd.to_datetime(data['ReleaseYear'], format='%Y', errors='coerce')

# Converting some data types as per required analysis
# Convert SoldFlag and SoldCount to integer
data['SoldFlag'] = data['SoldFlag'].astype(int)
data['SoldCount'] = data['SoldCount'].astype(int)

# Convert File_Type and MarketingType to category
data['File_Type'] = data['File_Type'].astype('category')
data['MarketingType'] = data['MarketingType'].astype('category')

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 198917 entries, 0 to 198916
Data columns (total 14 columns):
 #   Column            Non-Null Count   Dtype         
---  ------            --------------   -----         
 0   Order             198917 non-null  int64         
 1   File_Type         198917 non-null  category      
 2   SKU_number        198917 non-null  int64         
 3   SoldFlag          198917 non-null  int32         
 4   SoldCount         198917 non-null  int32         
 5   MarketingType     198917 non-null  category      
 6   ReleaseNumber     198917 non-null  int64         
 7   New_Release_Flag  198917 non-null  int64         
 8   StrengthFactor    198917 non-null  float64       
 9   PriceReg          198917 non-null  float64       
 10  ReleaseYear       198916 non-null  datetime64[ns]
 11  ItemCount         198917 non-null  int64         
 12  LowUserPrice      198917 non-null  float64       
 13  LowNetPrice       198917 non-null  float64       
dtypes: category(2), datetime64[ns](1), float64(4), int32(2), int64(5)
memory usage: 17.1 MB

4. Exploratory Data Analysis

A. Total Sales Over Time

import matplotlib.pyplot as plt
import seaborn as sns

# Set plot style
sns.set(style="whitegrid")

# Total sales over time
sales_over_time = data.groupby('ReleaseYear')['SoldCount'].sum().reset_index()

# Plot total sales over time
plt.figure(figsize=(12, 6))
sns.lineplot(data=sales_over_time, x='ReleaseYear', y='SoldCount', marker='o')
plt.title('Total Sales Over Time')
plt.xlabel('Year')
plt.ylabel('Total Sold Count')
plt.xticks(rotation=45)
plt.grid(True)
plt.show()

import numpy as np
import plotly.graph_objects as go

# Sales Trends Over Time
sales_trends = data.groupby('ReleaseYear')['SoldCount'].sum().reset_index()

sales_line_chart = go.Figure()

sales_line_chart.add_trace(go.Scatter(x=sales_trends['ReleaseYear'], y=sales_trends['SoldCount'], mode='lines+markers', name='Sales Trends'))
sales_line_chart.update_layout(title='Sales Trends Over Time',
                               xaxis_title='Year',
                               yaxis_title='Sold Count')
sales_line_chart.show()

C:\Users\yoshu\anaconda3\Lib\site-packages\_plotly_utils\basevalidators.py:106: FutureWarning:

The behavior of DatetimeProperties.to_pydatetime is deprecated, in a future version this will return a Series containing python datetime objects instead of an ndarray. To retain the old behavior, call `np.array` on the result

B. Sales Distribution by Year

# Sales distribution by year
plt.figure(figsize=(12, 6))
sns.histplot(data=data, x='ReleaseYear', bins=30, kde=True)
plt.title('Sales Distribution by Year')
plt.xlabel('Year')
plt.ylabel('Number of Sales')
plt.xticks(rotation=45)
plt.grid(True)
plt.show()

C. Identify Top-Selling Products

# Top-selling products
top_products = data.groupby('SKU_number')['SoldCount'].sum().reset_index().sort_values(by='SoldCount', ascending=False)

# Plot top-selling products
plt.figure(figsize=(12, 6))
sns.barplot(data=top_products.head(10), x='SKU_number', y='SoldCount',color='r')
plt.title('Top 10 Selling Products')
plt.xlabel('SKU Number')
plt.ylabel('Total Sold Count')
plt.grid(True)
plt.show()

D. Top-selling categories

top_categories = data.groupby('MarketingType')['SoldCount'].sum().reset_index().sort_values(by='SoldCount', ascending=True)

# Plot top-selling categories
plt.figure(figsize=(12, 6))
sns.barplot(data=top_categories, x='MarketingType', y='SoldCount')
plt.title('Top Selling Categories')
plt.xlabel('Marketing Type')
plt.ylabel('Total Sold Count')
plt.grid(True)
plt.show()

C:\Users\yoshu\AppData\Local\Temp\ipykernel_9688\2078425793.py:1: FutureWarning:

The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.

E. Investigate Stock Levels and Low-Stock Items

# Assuming ItemCount represents stock levels
low_stock_items = data[data['ItemCount'] < 10]  # Threshold for low stock can be adjusted

low_stock_summary = low_stock_items[['SKU_number', 'ItemCount']].sort_values(by='ItemCount')

low_stock_summary.head(10)

	SKU_number	ItemCount
41643	3763514	0
103059	2295426	0
42240	3775878	0
123224	2285510	0
123351	2277511	0
42095	3212060	0
42084	1438250	0
42004	3793730	0
41958	2627348	0
123595	2277021	0

F. Distribution of Sold Count On Year basis

import matplotlib.pyplot as plt
import pandas as pd

# Create a pivot table to aggregate 'SoldCount' by 'ReleaseYear'
pivot_table = pd.pivot_table(data, values='SoldCount', index='ReleaseYear', aggfunc='sum')

fig, ax = plt.subplots(figsize=(20, 8))
pivot_table.plot(kind='bar', ax=ax, color='red')

ax.set_title("Frequency Distribution of Sold Count by Year")
ax.set_xlabel("Release Year")
ax.set_ylabel("Total Sold Count")

plt.xticks(rotation=90) 
plt.tight_layout()       
plt.show()

# Define low stock threshold
low_stock_summary = 10

# Identify low-stock items
low_stock_items = data[data['ItemCount'] < low_stock_summary]

# Current stock levels
current_stock_levels = data.groupby('SKU_number')['ItemCount'].sum().reset_index()

# Calculate low-stock items percentage
low_stock_items_count = low_stock_items.shape[0]
total_items_count = data.shape[0]
low_stock_percentage = (low_stock_items_count / total_items_count) * 100

print(f"Low-stock items count: {low_stock_items_count}")
print(f"Percentage of low-stock items: {low_stock_percentage:.2f}%")

Low-stock items count: 3379
Percentage of low-stock items: 1.70%

# Plot current stock levels
plt.figure(figsize=(12, 6))
sns.histplot(data=current_stock_levels, x='ItemCount', bins=50, kde=True)
plt.title('Current Stock Levels Distribution')
plt.xlabel('Item Count')
plt.ylabel('Number of SKUs')
plt.grid(True)
plt.show()

# Visualization of stock levels
plt.figure(figsize=(12, 6))
plt.hist(data['ItemCount'], bins=20, color='skyblue', edgecolor='black')
plt.axvline(x=low_stock_summary, color='red', linestyle='--', label='Low Stock Threshold')
plt.title('Distribution of Stock Levels')
plt.xlabel('ItemCount')
plt.ylabel('Frequency')
plt.legend()
plt.grid(True)
plt.show()

# Sales trends over time (number of sales per year)
sales_per_year = data.groupby(data['ReleaseYear'].dt.year)['SoldCount'].sum()

# Plotting sales trends over time
plt.figure(figsize=(12, 6))
sns.lineplot(x=sales_per_year.index, y=sales_per_year.values)
plt.title('Sales Trends Over Time')
plt.xlabel('Year')
plt.ylabel('Total Sold Count')
plt.grid(True)
plt.show()

# Identify top-selling products by SKU_number
top_selling_products = data.groupby('SKU_number')['SoldCount'].sum().sort_values(ascending=False).head(10)

# Plotting top-selling products
plt.figure(figsize=(12, 6))
sns.barplot(x=top_selling_products.index, y=top_selling_products.values)
plt.title('Top-Selling Products')
plt.xlabel('SKU Number')
plt.ylabel('Total Sold Count')
plt.xticks(rotation=45)
plt.grid(True)
plt.show()

4. Inventory Insights

A. Calculate Inventory Turnover

# Calculate total sold count and average inventory level
total_sold_count = data['SoldCount'].sum()
average_inventory_level = data['ItemCount'].mean()

# Calculate inventory turnover
inventory_turnover = total_sold_count / average_inventory_level

print(f"Inventory Turnover: {inventory_turnover:.2f}")

Inventory Turnover: 591.27

import plotly.graph_objects as go

# Calculate inventory metrics
inventory_metrics = data[['ItemCount']].describe().loc[['mean', 'std', 'min', 'max']].reset_index()
inventory_metrics.columns = ['Metric', 'ItemCount']

inventory_bar_plot = go.Figure()

# Add a bar trace to the plot
inventory_bar_plot.add_trace(go.Bar(
    x=inventory_metrics['Metric'],
    y=inventory_metrics['ItemCount'],
    marker_color='skyblue',
    name='Inventory Metrics'
))

# Update the layout of the plot
inventory_bar_plot.update_layout(
    title='Inventory Metrics',
    xaxis_title='Metric',
    yaxis_title='Item Count',
    xaxis=dict(tickmode='array', tickvals=list(inventory_metrics['Metric'])),
    yaxis=dict(tickformat=".0f")  # Format y-axis as integers
)
inventory_bar_plot.show()

B. Calculate Stock-to-Sales Ratio

# Calculate total inventory and total sales
total_inventory = data['ItemCount'].sum()
total_sales = data['SoldCount'].sum()

# Calculate stock-to-sales ratio
stock_to_sales_ratio = total_inventory / total_sales

print(f"Stock-to-Sales Ratio: {stock_to_sales_ratio:.2f}")

Stock-to-Sales Ratio: 336.42

# Create a bar plot using Plotly to visualize the total inventory and total sales
fig = go.Figure(data=[
    go.Bar(name='Total Inventory', x=['Total Inventory'], y=[total_inventory]),
    go.Bar(name='Total Sales', x=['Total Sales'], y=[total_sales])
])

# Update layout for better visualization
fig.update_layout(
    title='Total Inventory vs. Total Sales',
    xaxis_title='Category',
    yaxis_title='Count',
    barmode='group'
)

# Add stock-to-sales ratio as a text annotation
fig.add_annotation(
    x=0.5,
    y=max(total_inventory, total_sales),
    text=f"Stock-to-Sales Ratio: {stock_to_sales_ratio:.2f}",
    showarrow=False,
    font=dict(size=14, color='black')
)

fig.show()

C. Calculate Reorder Points

# Assume a lead time of 14 days
lead_time_days = 14

# Calculate daily demand (assuming the dataset spans 365 days)
daily_demand = total_sales / 365

# Calculate Lead Time Demand
lead_time_demand = daily_demand * lead_time_days

# Estimate Safety Stock (e.g., 20% of lead time demand)
safety_stock = lead_time_demand * 0.20

# Calculate Reorder Point
reorder_point = lead_time_demand + safety_stock

print(f"Reorder Point: {reorder_point:.2f}")

Reorder Point: 1127.40

import seaborn as sns
import matplotlib.pyplot as plt

# Select only numeric columns from the DataFrame
numeric_data = data.select_dtypes(include=['float64', 'int64'])

# Calculate the correlation matrix
corr_matrix = numeric_data.corr()

plt.figure(figsize=(10, 8))

# Plot the heatmap of the correlation matrix
sns.heatmap(corr_matrix,
            annot=True,          # Display the correlation coefficients on the heatmap
            fmt='.2f',          # Format for displaying the correlation coefficients
            cmap='coolwarm',    # Color map for the heatmap
            xticklabels=corr_matrix.columns,
            yticklabels=corr_matrix.columns)

plt.title('Correlation Heatmap')
plt.show()

import matplotlib.pyplot as plt

# Count the frequency of each marketing type
marketing_type_counts = data['MarketingType'].value_counts()

plt.figure(figsize=(10, 6))
marketing_type_counts.plot(kind='bar', color='skyblue')

plt.title("Frequency Distribution of Marketing Type")
plt.xlabel("Marketing Type")
plt.ylabel("Frequency")
plt.show()

5. Recommendation regarding Inventory

# Aggregate sales data per product per month
data['OrderDate'] = pd.to_datetime(data['Order'])
data['YearMonth'] = data['OrderDate'].dt.to_period('M')
monthly_sales = data.groupby(['SKU_number', 'YearMonth'])['SoldCount'].sum().reset_index()
monthly_sales

	SKU_number	YearMonth	SoldCount
0	50001	1970-01	0
1	50002	1970-01	0
2	50003	1970-01	0
3	50004	1970-01	0
4	50005	1970-01	0
...	...	...	...
133355	3960560	1970-01	0
133356	3960724	1970-01	0
133357	3960742	1970-01	0
133358	3960760	1970-01	0
133359	3960788	1970-01	0

133360 rows × 3 columns

# Calculate Sales Value
data['SalesValue'] = data['SoldCount'] * data['PriceReg']

# Aggregate Sales Value per SKU
sku_sales = data.groupby('SKU_number')['SalesValue'].sum().reset_index()

# Rank SKUs by Sales Value
sku_sales['Rank'] = sku_sales['SalesValue'].rank(ascending=False)

# Determine ABC categories (top 20% as A, next 30% as B, rest as C)
total_skus = len(sku_sales)
sku_sales['Category'] = pd.cut(sku_sales['Rank'], bins=[0, 0.2*total_skus, 0.5*total_skus, total_skus], labels=['A', 'B', 'C'])

print(sku_sales.head(10))

   SKU_number  SalesValue     Rank Category
0       50001        0.00  73062.0        C
1       50002        0.00  73062.0        C
2       50003        0.00  73062.0        C
3       50004        0.00  73062.0        C
4       50005        0.00  73062.0        C
5       50006      194.95   4156.0        A
6       50009        0.00  73062.0        C
7       50011        0.00  73062.0        C
8       50012        0.00  73062.0        C
9       50013        0.00  73062.0        C

Low Stock Items

# Example of tracking inventory levels
inventory_levels = data.groupby('SKU_number')['ItemCount'].sum().reset_index()

# Set threshold for low stock
low_stock_threshold = 10
inventory_levels['LowStock'] = inventory_levels['ItemCount'] < low_stock_threshold

# Output low stock items for restocking
low_stock_items = inventory_levels[inventory_levels['LowStock']]
low_stock_items.head(10)

	SKU_number	ItemCount	LowStock
2780	53310	9	True
8050	105240	9	True
8075	105414	7	True
8573	107240	7	True
8634	107511	8	True
8733	108014	9	True
12609	118970	9	True
16430	138220	8	True
17863	145863	7	True
17880	145912	9	True

Calculate Key Performance Indicators (KPIs)

# Calculate Inventory Turnover (SoldCount / ItemCount)
# Adding a small value to ItemCount to avoid division by zero
data['InventoryTurnover'] = data['SoldCount'] / (data['ItemCount'] + 1e-5)

# Calculate Stock-to-Sales Ratio (ItemCount / SoldCount)
# Adding a small value to SoldCount to avoid division by zero
data['StockToSalesRatio'] = data['ItemCount'] / (data['SoldCount'] + 1e-5)

# Calculate Reorder Points
# Simple approach: If ItemCount is below a certain threshold, it's a reorder point
reorder_threshold = 10
data['ReorderPoint'] = data['ItemCount'] < reorder_threshold

# Summary of KPIs
inventory_kpis = data[['SKU_number', 'InventoryTurnover', 'StockToSalesRatio', 'ReorderPoint']].describe()

inventory_kpis

	SKU_number	InventoryTurnover	StockToSalesRatio
count	1.989170e+05	198917.000000	1.989170e+05
mean	8.613626e+05	1.008128	3.728885e+06
std	8.699794e+05	448.429360	3.603524e+06
min	5.000100e+04	0.000000	0.000000e+00
25%	2.172520e+05	0.000000	1.900000e+06
50%	6.122080e+05	0.000000	2.900000e+06
75%	9.047510e+05	0.000000	4.600000e+06
max	3.960788e+06	200000.000000	2.542000e+08

Recommendations based on inventory turnover and stock-to-sales ratio

# High inventory turnover and low stock-to-sales ratio indicate efficient inventory management
recommendations = data[(data['InventoryTurnover'] > data['InventoryTurnover'].mean()) & 
                       (data['StockToSalesRatio'] < data['StockToSalesRatio'].mean())]

# Displaying top 10 recommendations
print(recommendations.head(10))

       Order   File_Type  SKU_number  SoldFlag  SoldCount MarketingType  \
4615    4836  Historical     2277016         1          2             D   
16465  16865  Historical      613864         1         69             D   
26488  27073  Historical     1475488         1         12             D   
46039  51916  Historical      809190         1         12             S   

       ReleaseNumber  New_Release_Flag  StrengthFactor  PriceReg ReleaseYear  \
4615               5                 1        276684.0      0.00  2014-01-01   
16465              0                 0          7237.0     80.75  2012-01-01   
26488              2                 1       1448498.0     72.74  2013-01-01   
46039              5                 1        724822.0     40.00  2012-01-01   

       ItemCount  LowUserPrice  LowNetPrice                     OrderDate  \
4615           0          0.00       119.48 1970-01-01 00:00:00.000004836   
16465         44          0.00         0.00 1970-01-01 00:00:00.000016865   
26488          8        141.25        54.03 1970-01-01 00:00:00.000027073   
46039         11        142.99        43.99 1970-01-01 00:00:00.000051916   

      YearMonth  SalesValue  InventoryTurnover  StockToSalesRatio  \
4615    1970-01        0.00      200000.000000           0.000000   
16465   1970-01     5571.75           1.568181           0.637681   
26488   1970-01      872.88           1.499998           0.666666   
46039   1970-01      480.00           1.090908           0.916666   

       ReorderPoint  
4615           True  
16465         False  
26488          True  
46039         False  

Actionable Recommendations for Optimizing Inventory Management

Actionable Recommendations for Optimizing Inventory Management

1. Accurate Demand Forecasting

• Utilize Historical Sales Data: Leverage historical sales data to improve demand forecasting accuracy. Employ statistical techniques such as time series analysis or advanced machine learning algorithms to predict future sales trends more precisely.
• Advanced Forecasting Methods: Consider integrating predictive analytics and modeling tools to anticipate demand fluctuations and seasonal variations, which will help in planning inventory levels more effectively.

2. Demand-Driven Inventory Management

• Dynamic Inventory Adjustments: Shift from static reorder points to a demand-driven approach. Adjust inventory levels dynamically based on real-time sales data and updated demand forecasts to align inventory with actual market needs.
• Real-Time Data Integration: Implement systems that integrate real-time sales data into inventory management practices to ensure timely and responsive inventory adjustments.

3. Optimization of Stock Levels

• Analyze Sales Patterns: Regularly review sales data to identify slow-moving or obsolete inventory items. Implement strategies to reduce stock for items with declining demand.
• Resource Reallocation: Reallocate inventory resources to high-demand products to ensure that fast-moving items are sufficiently stocked, thus enhancing turnover and reducing storage costs.

4. Just-in-Time (JIT) Inventory Implementation

• Reduce Excess Inventory: Adopt a Just-in-Time (JIT) inventory strategy, where inventory is ordered and replenished only as needed. This approach minimizes excess inventory, reduces storage costs, and improves cash flow.
• Timely Replenishment: Ensure that products are available when required by aligning inventory replenishment closely with actual demand.

5. Inventory Segmentation

• Categorize Inventory: Segment inventory based on factors such as sales velocity, profitability, and demand characteristics. Create distinct inventory management strategies for each segment.
• Tailored Management Approaches: Apply different inventory management practices tailored to each segment’s specific needs to optimize inventory levels and minimize holding costs.

6. Supplier Collaboration

• Enhance Supplier Relations: Foster strong partnerships with suppliers to streamline the supply chain and reduce lead times. Establish collaborative practices such as vendor-managed inventory (VMI) or consignment inventory arrangements to ensure timely stock replenishment.
• Reduce Lead Times: Work with suppliers to shorten lead times and improve the efficiency of the supply chain.

7. Regular Monitoring of Inventory Metrics

• Track Key Metrics: Continuously monitor critical inventory metrics, including inventory turnover rates, stock-to-sales ratios, and reorder points. Regularly review these metrics to identify deviations from expected performance.
• Take Corrective Actions: Implement corrective measures based on the insights gained from monitoring metrics to address any inventory management issues promptly.

8. Invest in Technology

• Adopt Inventory Management Software: Invest in advanced inventory management systems or enterprise resource planning (ERP) solutions that offer real-time visibility into inventory levels, sales trends, and supply chain data.
• Leverage Advanced Analytics: Utilize the analytics and automation features of these technologies to enhance decision-making and optimize inventory management processes.

9. Effective Promotions and Discounts

• Plan Promotions Strategically: Use sales data to plan targeted promotions and discounts. Offer promotions on slow-moving items to clear excess inventory and free up storage space.
• Drive Sales with Discounts: Implement discount strategies to boost sales of underperforming items while maintaining overall inventory balance.

10. Continuous Improvement

• Foster a Culture of Improvement: Develop a culture focused on continuous improvement in inventory management practices. Regularly review and refine inventory policies, processes, and strategies based on feedback and changing market conditions.
• Adapt to Market Dynamics: Stay adaptable to evolving market dynamics and adjust inventory management practices accordingly to maintain competitiveness and operational efficiency.

By implementing these recommendations, businesses can achieve optimized inventory management, leading to improved operational efficiency, cost reduction, and enhanced customer satisfaction. Regularly reviewing and adjusting strategies in response to sales patterns and market trends is essential for maintaining a competitive edge in a dynamic business environment.